kd-trees are space-partitioning data structures which provide for an efficient data model when performing range searching in multiple dimensions (hence the name kd, where ‛‛k’’ referes to the number of dimensions). CGAL has implemented various flavours of these trees in the ‛‛Spatial Searching’’ package, amongst other things. This effort is an attempt to create a python interface to the simplest 2d and 3d implementation of CGAL, using Boost.Python
These bindings provide for exact as well as fuzzy searching in hypercubes and hyperspheres in two and three dimensions, using the existing kernels in the CGAL-python project.
News20080613: The code has been submitted to CGAL-Pythons' SVN server, under the (testing) branch spatial_searching-branch.
About the Demo
To run select the desired radio buttons and fill the textboxes with 2d or 3d comma separated co-ordinate values accordingly.|
Please be patient while the tests run.
This is merely a humble web server! Spatial_seaching_2:
Exact range search in a fixed file of 50k 2d points xmin=ymin=0
Exact range search in a random file of 50k 3d points xmin=ymin=0
|William Vassilis Karageorgos|
March 2008, Athens